In a typical software application, testing is generally performed by quality assurance teams. A quality assurance team may create a functional equivalent system that represents a copy of the software application. The quality assurance team may then test the functional equivalent system by executing a suit of test cases on the functional equivalent system in a testing environment. For example, the suit of test cases may include black box or white box testing methodologies. Further, the testing environment is designed to mimic a real-world production environment in which the software application runs on a day-to-day basis. However, functional equivalent systems are generally not scalable for today's real-world big-data environments. Traditional testing environments and test cases do not accurately replicate the large scale and highly variable characteristics of today's Internet traffic. For instance, a software-as-a-service (SaaS) application processes massive amounts of dynamically-changing data that flows in and out of the environment at a very fast rate. The typical suit of test cases executing on functional equivalent systems does not realistically simulate this uncertain nature of the massive amounts of data. Thus, testing functional equivalent systems in big-data environments does not accurately provide insight on the health of the application or potential issues that may arise in the production environment.